Building your own RAG application using Together AI and Langchain

Together AI has launched the Together Embeddings endpoint, enabling users to build their own powerful RAG-based applications directly from the platform using Langchain. RAG (Retrieval Augmented Generation) combines generative models and retrieval models for knowledge-intensive tasks, improving performance and accuracy by leveraging external data sources during response generation. Building a RAG system can be cost and data efficient without requiring technical expertise to train a model, and fine-tuning an embedding or generative model can further improve the quality of the solution. The process involves creating a vector store using an embedding model, retrieving relevant data examples, augmenting the information with a prompt, and obtaining the final output from a generative model. An example demonstrates how to incorporate recent knowledge into a RAG application using the Together API and Langchain, providing accurate and up-to-date responses compared to relying on pre-trained models.